Face recognition is a task that is predominately performed using deep convolutional neural networks to create an embedding from an input face. The adjacent task of face verification (determining if two face images belong to the same identity or not) can then be performed by assessing the distance between two embeddings. There are two main methods for training this embedding, either by directly learning an embedding, as in the case of the triplet loss, or by training a multi-class classifier. The latter is the method of choice in the algorithm ArcFace [1]}, which we use in this paper.
